In the use stage, insights gained from data analysis are applied to to create value with data. This stage may also involve changing or adding to the data based on your analysis.
One of the most common uses of data is to support decision-making.
For example, here are a few of the many decisions data can help make:
There are many other ways to create value with data, most of which involve at least one form of data analysis (using data to describe, recommend, diagnose, discover, or decide, for example). Here are some strategies to start with:
In the automotive example, the manufacturer might update collision avoidance and navigation algorithms across all vehicles based on raw data from various cars on the road and the analysis performed on it.
One of the most crucial building blocks of data-driven value is the API. API stands for an application program interface. It’s a standardized way to pass data and commands between various systems to help them work together stably and securely.
APIs enable developers to connect apps and devices without reinventing common functions or merging different operations. APIs require robust architecture and clarity about how the data is structured; otherwise, apps that read or alter the data in different ways could have unintended results.
For example, any time you use Facebook to log into another website, like Airbnb, you're using an authentication API. Facebook confirms to Airbnb that you're who you say, without you having to set up a separate username and password. There are rules about what data can and cannot be passed back and forth between systems with Facebook—which can either protect or compromise your privacy, depending on the situation.
Third-party apps don't necessarily get to access everything about your Facebook profile and might only be able to see, for example, your email address.
APIs allow you to use data quickly without needing to build a lot of new infrastructure. Once you have determined your intended use of data, consider whether you could use an API and related software development kits (SDKs) to make it happen quickly.
APIs also enable integrations related to ‘smart’ or connected homes. Many connected homes use Amazon Alexa, Google Assistant, or Apple HomeKit (Siri) to provide an interface, including voice commands, to control their home and provide multi-step automation like ‘turn on the lights whenever the security camera detects motion.’
Google's team created a video called ‘Works With Nest’ explaining how APIs coordinate Google's Nest devices and third-party devices to make the ‘connected home’ easier—without ever mentioning APIs. When APIs are implemented well, we don't notice they're there.
If you use smart home gadgets, you may have noticed that the proprietary app has more features than the generic controls accessed through a hub system like Apple or Google.
Sometimes, this is because not all functions are easy to turn into an API. Other times, it may be a ‘special sauce’ feature that the manufacturer is reserving for themselves to stay differentiated. Similarly, data which is passed to third-party services may be limited. Not all data should be made available through ‘external’ APIs due to concerns over privacy, proprietary insight, or data sovereignty.
APIs are used inside organizations, too, or only between key partners. This is usually called a private API.
APIs can accelerate innovation, but many organizations didn't have the foresight, resources, or alignment to consider this when building their technology stack or infrastructure. Later, when they try to develop APIs, they find it tough to catch up to other organizations that have had a higher level of digital fluency for longer.
Amazon's leaders are often attributed with saying, 'you can use any technology you want, as long as it connects to everything else,' a principle that used API and data-centric thinking long before competitors were considering e-commerce. This is part of why it’s important to have shared language about the data supply chain—so that you and your colleagues are thinking as early as possible about the ways that your data will connect with everything else.
An example of the power and utility of APIs is a do-it-yourself automation tool called Zapier. Zapier started as a relatively basic API integration platform, but its capabilities are approaching enterprise-level robustness. Zapier organizes APIs into ‘triggers’ and ‘actions’ which can be connected in ‘zaps.’ Currently, there are over 2000 apps in their ecosystem. For example, a user might create a simple ‘zap’ where every time they send a message through Gmail that meets certain criteria, Zapier is ‘triggered’ to save that email to a row in a spreadsheet in Excel. Zapier is a great way to experiment with your own API-backed functions. Even if you can’t access it at work for security reasons, you can play with it at home, making a simple zap between a Google account and a Facebook account to see how you would move data from one system to another automatically—in other words, how you would build a simple algorithm.
You can also integrate building blocks of more complex algorithms. At our company we used Zapier and Google to automate the process of recording new contacts. If we scan a business card and upload the image to our customer relationship management (CRM) database, Zapier will pass that image to Google's image recognition API. Then Google analyzes the card image and extracts text and logos as well as specific companies and names, and then passes that information back to our CRM. It allows us to quickly make scans searchable without paying someone to go through and do all of that rote work—instead, they can focus their work on more human-centric tasks. In this way, APIs allow you to leverage more complex machine learning that someone else has developed, rather than trying to do it all ‘in-house.’
Clearbit is an example of a data enrichment API. Clearbit acquires, aggregates and normalizes data about individuals, especially professionals. It does this by offering a free service to individuals in a two-way model. In exchange for sharing their address books with the company, their address books are updated with the most up-to-date contact, title and employment history. Clearbit then monetizes that data by making a premium, one-way version available to larger companies who get enriched data without having to provide data in turn.
Another more specific API example from the finance world is Plaid. Plaid helps developers make financial apps without developing a lot of specific partnerships. If you use an app that integrates with your bank account, such as Venmo, you’ve used Plaid. You can link these services to your bank because Plaid has normalized, or abstracted, the APIs of many financial institutions.
Plaid provides several narrow financial data services, often referred to generically as microservices. Each function does just one thing, but it does it well and quickly. Plaid allows a developer to rapidly access an end user’s income, detect fraud, verify employment, view assets, check creditworthiness, verify identity, and much more. So, when a financial app request like “get a user’s net worth” is decomposed into many specific questions, Plaid can deliver reliable results.
When Plaid passes data to a developer, the developer can do what they do best, which might have little to do with financial institutions. For example, developers of a personal budgeting app don't need to check credit but need to know enough to make savings recommendations or create a helpful infographic.
Plaid and other API normalization services are taking the heavy lifting out of app development. Instead of building digital value from scratch, developers can assemble off-the-shelf building blocks. This is often the only way an ‘incumbent’ competitor who has not had much digital momentum can catch up with digital-first ‘disruptors’ in their space.
Sometimes an adjacent dataset can be used to infer something about a ‘traditional’ dataset. This is a specific category of use that is ripe with opportunities but also full of ethical considerations.
For example, retail stores traditionally report their sales results in the quarter after the holidays, leaving investors to wonder about performance for an uncomfortable length of time. However, many retail stores have parking lots. Parking lot occupancy seen from satellites or security cameras can be cross-referenced to nearby stores to guess future earnings results.
If we ask the basic question of “What will a retail store’s holiday sales numbers be?” we might have to wait until a past-looking report is issued. To get information faster, we need to use a computational thinking approach to decompose the result (sales) into the steps leading up to it (number of potential customers in stores, which can be inferred by how full parking lots are).
By pushing ourselves to see the root cause of or correlations to those sales, like increased numbers of shoppers, we could get some useful information sooner. This mindset lets us know what sales volume we might expect before getting the quarterly report months after the holidays.
